International Conference on Computer and Knowledge Engineering

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Home / 14th International Conference on Computer and Knowledge Engineering

Non-Negative Matrix Factorization improves Residual Neural Networks

Authors :

Hojjat Moayed¹

1- Esfarayen University of Technology

Keywords :

ResNet،Residual Neural Network،NMF،Deep Learning

Abstract :

Residual neural networks enable the use of very deep architectures. These architectures benefit by passing the identity information from a layer directly to subsequent layers. Extensive research has been conducted to improve the performance of residual neural networks. In this paper, we propose a method to improve performance by providing a more informative input using non-negative matrix factorization. The method combines the invariant features learned from the training data with the extracted, fine-tuned features at the end of the residual block. Our experimental results confirm that the proposed architecture improves performance on the image classification task.

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